Overview

Dataset statistics

Number of variables35
Number of observations5000
Missing cells10130
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory636.0 B

Variable types

Numeric13
Categorical11
Text3
DateTime3
Boolean4
Unsupported1

Alerts

aborteddataerrorcount has constant value ""Constant
accommodationunavailableerrorcount has constant value ""Constant
failureindurationserrorcount has constant value ""Constant
createtripformsubmission_fastjourney has constant value ""Constant
overnightreductionerrorcount has constant value ""Constant
createtripformsubmission_childcount is highly imbalanced (57.7%)Imbalance
createtripformsubmission_closestcity is highly imbalanced (54.3%)Imbalance
createtripformsubmission_directjourney is highly imbalanced (99.7%)Imbalance
createtripformsubmission_extrainfo is highly imbalanced (99.3%)Imbalance
nogoodscoreerrorcount is highly imbalanced (94.9%)Imbalance
success is highly imbalanced (82.6%)Imbalance
timeouterrorcount is highly imbalanced (70.8%)Imbalance
transportunavailableerrorcount is highly imbalanced (99.7%)Imbalance
creationdate has 130 (2.6%) missing valuesMissing
failtimeseconds has 4870 (97.4%) missing valuesMissing
jotform_onewaytrip has 5000 (100.0%) missing valuesMissing
tripbuildtimeseconds has 130 (2.6%) missing valuesMissing
firstchoiceaccommodationunavailablecount is highly skewed (γ1 = 22.72164325)Skewed
noavailabledotwaccommodationerrorcount is highly skewed (γ1 = 22.06647453)Skewed
nofamilymanualfallbackserrorcount is highly skewed (γ1 = 37.4528303)Skewed
substituteaccommodationunavailablecount is highly skewed (γ1 = 22.77448326)Skewed
tripbuildtimeseconds is highly skewed (γ1 = 23.38040554)Skewed
createtripid_region has unique valuesUnique
jotform_onewaytrip is an unsupported type, check if it needs cleaning or further analysisUnsupported
badproportionerrorcount has 4547 (90.9%) zerosZeros
firstchoiceaccommodationunavailablecount has 1760 (35.2%) zerosZeros
noavailabledotwaccommodationerrorcount has 4861 (97.2%) zerosZeros
nofamilymanualfallbackserrorcount has 4995 (99.9%) zerosZeros
norouteserrorcount has 3135 (62.7%) zerosZeros
substituteaccommodationunavailablecount has 4773 (95.5%) zerosZeros

Reproduction

Analysis started2024-02-04 09:21:40.122273
Analysis finished2024-02-04 09:21:57.533064
Duration17.41 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

createtripid
Real number (ℝ)

Distinct2801
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6009212 × 1014
Minimum6.0001285 × 1014
Maximum9.2228048 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:21:57.608790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6.0001285 × 1014
5-th percentile6.1609005 × 1014
Q16.7918541 × 1014
median7.5937461 × 1014
Q38.387415 × 1014
95-th percentile9.0617686 × 1014
Maximum9.2228048 × 1014
Range3.2226763 × 1014
Interquartile range (IQR)1.5955609 × 1014

Descriptive statistics

Standard deviation9.3126107 × 1013
Coefficient of variation (CV)0.1225195
Kurtosis-1.193467
Mean7.6009212 × 1014
Median Absolute Deviation (MAD)7.9707081 × 1013
Skewness0.024331046
Sum3.8004606 × 1018
Variance8.6724717 × 1027
MonotonicityNot monotonic
2024-02-04T09:21:57.743370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.780682326 × 101411
 
0.2%
6.144077872 × 101410
 
0.2%
8.733860145 × 10149
 
0.2%
6.859121871 × 10149
 
0.2%
6.325074452 × 10148
 
0.2%
8.917458881 × 10148
 
0.2%
7.945427677 × 10148
 
0.2%
9.222804796 × 10148
 
0.2%
8.603450218 × 10147
 
0.1%
7.119398435 × 10147
 
0.1%
Other values (2791) 4915
98.3%
ValueCountFrequency (%)
6.000128507 × 10141
< 0.1%
6.001193436 × 10141
< 0.1%
6.005894494 × 10141
< 0.1%
6.007711331 × 10141
< 0.1%
6.009363974 × 10142
< 0.1%
6.010832743 × 10142
< 0.1%
6.010949153 × 10141
< 0.1%
6.01173187 × 10141
< 0.1%
6.014506832 × 10141
< 0.1%
6.01476539 × 10141
< 0.1%
ValueCountFrequency (%)
9.222804796 × 10148
0.2%
9.221832521 × 10142
 
< 0.1%
9.220981713 × 10142
 
< 0.1%
9.220771286 × 10145
0.1%
9.217798978 × 10141
 
< 0.1%
9.217496784 × 10141
 
< 0.1%
9.214071218 × 10143
 
0.1%
9.212305815 × 10141
 
< 0.1%
9.21134385 × 10141
 
< 0.1%
9.210759547 × 10143
 
0.1%

region
Categorical

Distinct26
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size344.5 KiB
Highlands & Islands
838 
Italian Riviera
538 
Northern Italy
534 
Alsace
505 
North & Central Spain
416 
Other values (21)
2169 

Length

Max length21
Median length15
Mean length13.533
Min length5

Characters and Unicode

Total characters67665
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowSouthern Italy
2nd rowItalian Riviera
3rd rowHighlands & Islands
4th rowEast Highlands
5th rowYorkshire

Common Values

ValueCountFrequency (%)
Highlands & Islands 838
16.8%
Italian Riviera 538
10.8%
Northern Italy 534
10.7%
Alsace 505
10.1%
North & Central Spain 416
8.3%
East Highlands 360
 
7.2%
Southern Italy 317
 
6.3%
Switzerland 224
 
4.5%
Andalusia 186
 
3.7%
French Riviera 162
 
3.2%
Other values (16) 920
18.4%

Length

2024-02-04T09:21:57.877027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1254
12.6%
highlands 1198
12.0%
italy 851
 
8.5%
islands 838
 
8.4%
riviera 700
 
7.0%
italian 538
 
5.4%
northern 534
 
5.4%
alsace 505
 
5.1%
north 440
 
4.4%
spain 417
 
4.2%
Other values (24) 2698
27.1%

Most occurring characters

ValueCountFrequency (%)
a 7688
 
11.4%
l 5383
 
8.0%
n 5337
 
7.9%
4973
 
7.3%
i 4485
 
6.6%
s 4464
 
6.6%
t 4195
 
6.2%
r 4071
 
6.0%
e 3599
 
5.3%
h 2940
 
4.3%
Other values (29) 20530
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52524
77.6%
Uppercase Letter 8784
 
13.0%
Space Separator 4973
 
7.3%
Other Punctuation 1254
 
1.9%
Dash Punctuation 130
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7688
14.6%
l 5383
10.2%
n 5337
10.2%
i 4485
8.5%
s 4464
8.5%
t 4195
8.0%
r 4071
7.8%
e 3599
6.9%
h 2940
 
5.6%
d 2624
 
5.0%
Other values (12) 7738
14.7%
Uppercase Letter
ValueCountFrequency (%)
I 2227
25.4%
H 1263
14.4%
N 1088
12.4%
S 1058
12.0%
C 756
 
8.6%
R 701
 
8.0%
A 691
 
7.9%
E 360
 
4.1%
F 227
 
2.6%
Y 145
 
1.7%
Other values (4) 268
 
3.1%
Space Separator
ValueCountFrequency (%)
4973
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1254
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61308
90.6%
Common 6357
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7688
12.5%
l 5383
 
8.8%
n 5337
 
8.7%
i 4485
 
7.3%
s 4464
 
7.3%
t 4195
 
6.8%
r 4071
 
6.6%
e 3599
 
5.9%
h 2940
 
4.8%
d 2624
 
4.3%
Other values (26) 16522
26.9%
Common
ValueCountFrequency (%)
4973
78.2%
& 1254
 
19.7%
- 130
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7688
 
11.4%
l 5383
 
8.0%
n 5337
 
7.9%
4973
 
7.3%
i 4485
 
6.6%
s 4464
 
6.6%
t 4195
 
6.2%
r 4071
 
6.0%
e 3599
 
5.3%
h 2940
 
4.3%
Other values (29) 20530
30.3%

createtripid_region
Text

UNIQUE 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size422.6 KiB
2024-02-04T09:21:57.986936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length37
Median length31
Mean length29.533
Min length21

Characters and Unicode

Total characters147665
Distinct characters50
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5000 ?
Unique (%)100.0%

Sample

1st row891585467457763_Southern Italy
2nd row891585467457763_Italian Riviera
3rd row856446659120383_Highlands & Islands
4th row856446659120383_East Highlands
5th row643110738642468_Yorkshire
ValueCountFrequency (%)
1254
 
12.6%
italy 851
 
8.5%
islands 838
 
8.4%
riviera 700
 
7.0%
spain 416
 
4.2%
central 416
 
4.2%
highlands 360
 
3.6%
country 83
 
0.8%
downs 30
 
0.3%
wales 24
 
0.2%
Other values (5001) 5001
50.1%
2024-02-04T09:21:58.218134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 8647
 
5.9%
6 8547
 
5.8%
8 8546
 
5.8%
a 7688
 
5.2%
2 7267
 
4.9%
4 7129
 
4.8%
9 7114
 
4.8%
0 7030
 
4.8%
5 7025
 
4.8%
1 6994
 
4.7%
Other values (40) 71678
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75000
50.8%
Lowercase Letter 52524
35.6%
Uppercase Letter 8784
 
5.9%
Connector Punctuation 5000
 
3.4%
Space Separator 4973
 
3.4%
Other Punctuation 1254
 
0.8%
Dash Punctuation 130
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7688
14.6%
l 5383
10.2%
n 5337
10.2%
i 4485
8.5%
s 4464
8.5%
t 4195
8.0%
r 4071
7.8%
e 3599
6.9%
h 2940
 
5.6%
d 2624
 
5.0%
Other values (12) 7738
14.7%
Uppercase Letter
ValueCountFrequency (%)
I 2227
25.4%
H 1263
14.4%
N 1088
12.4%
S 1058
12.0%
C 756
 
8.6%
R 701
 
8.0%
A 691
 
7.9%
E 360
 
4.1%
F 227
 
2.6%
Y 145
 
1.7%
Other values (4) 268
 
3.1%
Decimal Number
ValueCountFrequency (%)
7 8647
11.5%
6 8547
11.4%
8 8546
11.4%
2 7267
9.7%
4 7129
9.5%
9 7114
9.5%
0 7030
9.4%
5 7025
9.4%
1 6994
9.3%
3 6701
8.9%
Connector Punctuation
ValueCountFrequency (%)
_ 5000
100.0%
Space Separator
ValueCountFrequency (%)
4973
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1254
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 86357
58.5%
Latin 61308
41.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7688
12.5%
l 5383
 
8.8%
n 5337
 
8.7%
i 4485
 
7.3%
s 4464
 
7.3%
t 4195
 
6.8%
r 4071
 
6.6%
e 3599
 
5.9%
h 2940
 
4.8%
d 2624
 
4.3%
Other values (26) 16522
26.9%
Common
ValueCountFrequency (%)
7 8647
10.0%
6 8547
9.9%
8 8546
9.9%
2 7267
8.4%
4 7129
8.3%
9 7114
8.2%
0 7030
8.1%
5 7025
8.1%
1 6994
8.1%
3 6701
7.8%
Other values (4) 11357
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 8647
 
5.9%
6 8547
 
5.8%
8 8546
 
5.8%
a 7688
 
5.2%
2 7267
 
4.9%
4 7129
 
4.8%
9 7114
 
4.8%
0 7030
 
4.8%
5 7025
 
4.8%
1 6994
 
4.7%
Other values (40) 71678
48.5%
Distinct2801
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Minimum2024-01-20 10:44:58.368000
Maximum2024-01-25 00:44:32.072000
2024-02-04T09:21:58.341204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:58.478288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

aborteddataerrorcount
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.3 KiB
0
5000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5000
100.0%

Length

2024-02-04T09:21:58.607145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:21:58.693290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5000
100.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.3 KiB
0
5000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5000
100.0%

Length

2024-02-04T09:21:58.781214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:21:58.858641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5000
100.0%
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1076
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:21:58.930826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.77778096
Coefficient of variation (CV)0.36903633
Kurtosis6.5728088
Mean2.1076
Median Absolute Deviation (MAD)0
Skewness2.0439263
Sum10538
Variance0.60494323
MonotonicityNot monotonic
2024-02-04T09:21:59.027938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 3741
74.8%
1 609
 
12.2%
4 303
 
6.1%
3 273
 
5.5%
6 46
 
0.9%
5 28
 
0.6%
ValueCountFrequency (%)
1 609
 
12.2%
2 3741
74.8%
3 273
 
5.5%
4 303
 
6.1%
5 28
 
0.6%
6 46
 
0.9%
ValueCountFrequency (%)
6 46
 
0.9%
5 28
 
0.6%
4 303
 
6.1%
3 273
 
5.5%
2 3741
74.8%
1 609
 
12.2%

badproportionerrorcount
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4
Minimum0
Maximum88
Zeros4547
Zeros (%)90.9%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:21:59.131891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum88
Range88
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.3992221
Coefficient of variation (CV)8.4980553
Kurtosis361.87438
Mean0.4
Median Absolute Deviation (MAD)0
Skewness17.276506
Sum2000
Variance11.554711
MonotonicityNot monotonic
2024-02-04T09:21:59.405839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 4547
90.9%
1 226
 
4.5%
2 86
 
1.7%
3 59
 
1.2%
4 23
 
0.5%
5 11
 
0.2%
6 6
 
0.1%
14 4
 
0.1%
40 3
 
0.1%
26 2
 
< 0.1%
Other values (22) 33
 
0.7%
ValueCountFrequency (%)
0 4547
90.9%
1 226
 
4.5%
2 86
 
1.7%
3 59
 
1.2%
4 23
 
0.5%
5 11
 
0.2%
6 6
 
0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
88 1
 
< 0.1%
86 2
< 0.1%
80 1
 
< 0.1%
48 2
< 0.1%
44 1
 
< 0.1%
43 1
 
< 0.1%
42 1
 
< 0.1%
40 3
0.1%
37 2
< 0.1%
32 1
 
< 0.1%
Distinct110
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size291.3 KiB
2024-02-04T09:21:59.558696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length2.6262
Min length2

Characters and Unicode

Total characters13131
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.4%

Sample

1st row{}
2nd row{}
3rd row{}
4th row{}
5th row{}
ValueCountFrequency (%)
3997
79.9%
15 100
 
2.0%
13 88
 
1.8%
14 70
 
1.4%
12 49
 
1.0%
9 40
 
0.8%
7 40
 
0.8%
11 38
 
0.8%
6 37
 
0.7%
8 34
 
0.7%
Other values (100) 507
 
10.1%
2024-02-04T09:21:59.832036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
{ 5000
38.1%
} 5000
38.1%
1 1182
 
9.0%
, 548
 
4.2%
5 259
 
2.0%
3 233
 
1.8%
9 174
 
1.3%
2 171
 
1.3%
4 142
 
1.1%
0 133
 
1.0%
Other values (3) 289
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Open Punctuation 5000
38.1%
Close Punctuation 5000
38.1%
Decimal Number 2583
19.7%
Other Punctuation 548
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1182
45.8%
5 259
 
10.0%
3 233
 
9.0%
9 174
 
6.7%
2 171
 
6.6%
4 142
 
5.5%
0 133
 
5.1%
7 120
 
4.6%
8 96
 
3.7%
6 73
 
2.8%
Open Punctuation
ValueCountFrequency (%)
{ 5000
100.0%
Close Punctuation
ValueCountFrequency (%)
} 5000
100.0%
Other Punctuation
ValueCountFrequency (%)
, 548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13131
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
{ 5000
38.1%
} 5000
38.1%
1 1182
 
9.0%
, 548
 
4.2%
5 259
 
2.0%
3 233
 
1.8%
9 174
 
1.3%
2 171
 
1.3%
4 142
 
1.1%
0 133
 
1.0%
Other values (3) 289
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
{ 5000
38.1%
} 5000
38.1%
1 1182
 
9.0%
, 548
 
4.2%
5 259
 
2.0%
3 233
 
1.8%
9 174
 
1.3%
2 171
 
1.3%
4 142
 
1.1%
0 133
 
1.0%
Other values (3) 289
 
2.2%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size283.3 KiB
0
4002 
1
526 
2
401 
3
 
67
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4002
80.0%
1 526
 
10.5%
2 401
 
8.0%
3 67
 
1.3%
4 4
 
0.1%

Length

2024-02-04T09:21:59.947795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:00.038368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4002
80.0%
1 526
 
10.5%
2 401
 
8.0%
3 67
 
1.3%
4 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 4002
80.0%
1 526
 
10.5%
2 401
 
8.0%
3 67
 
1.3%
4 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4002
80.0%
1 526
 
10.5%
2 401
 
8.0%
3 67
 
1.3%
4 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4002
80.0%
1 526
 
10.5%
2 401
 
8.0%
3 67
 
1.3%
4 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4002
80.0%
1 526
 
10.5%
2 401
 
8.0%
3 67
 
1.3%
4 4
 
0.1%
Distinct48
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31547490
Minimum-1
Maximum8.3521155 × 108
Zeros0
Zeros (%)0.0%
Negative55
Negative (%)1.1%
Memory size39.2 KiB
2024-02-04T09:22:00.157036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile26
Q126
median26
Q33117
95-th percentile1.8393659 × 108
Maximum8.3521155 × 108
Range8.3521155 × 108
Interquartile range (IQR)3091

Descriptive statistics

Standard deviation1.2766796 × 108
Coefficient of variation (CV)4.0468501
Kurtosis28.970181
Mean31547490
Median Absolute Deviation (MAD)0
Skewness5.3635578
Sum1.5773745 × 1011
Variance1.6299109 × 1016
MonotonicityNot monotonic
2024-02-04T09:22:00.287942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
26 3255
65.1%
22371672 276
 
5.5%
34 180
 
3.6%
28 144
 
2.9%
22370660 110
 
2.2%
22371862 102
 
2.0%
183936590 81
 
1.6%
29 75
 
1.5%
42771065 57
 
1.1%
-1 55
 
1.1%
Other values (38) 665
 
13.3%
ValueCountFrequency (%)
-1 55
 
1.1%
26 3255
65.1%
28 144
 
2.9%
29 75
 
1.5%
34 180
 
3.6%
40 27
 
0.5%
51 6
 
0.1%
3117 50
 
1.0%
1635547 25
 
0.5%
8900175 13
 
0.3%
ValueCountFrequency (%)
835211552 21
0.4%
834890881 17
0.3%
834890871 3
 
0.1%
834890865 9
 
0.2%
834890845 15
0.3%
833158856 24
0.5%
663365563 9
 
0.2%
650404624 21
0.4%
571902488 3
 
0.1%
394536322 2
 
< 0.1%
Distinct48
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size311.2 KiB
London
3255 
Paris
 
276
Manchester
 
180
Birmingham
 
144
Newcastle
 
110
Other values (43)
1035 

Length

Max length17
Median length6
Mean length6.706
Min length4

Characters and Unicode

Total characters33530
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLondon
2nd rowLondon
3rd rowCoventry
4th rowCoventry
5th rowBirmingham

Common Values

ValueCountFrequency (%)
London 3255
65.1%
Paris 276
 
5.5%
Manchester 180
 
3.6%
Birmingham 144
 
2.9%
Newcastle 110
 
2.2%
Edinburgh 102
 
2.0%
Cardiff 81
 
1.6%
Bristol 75
 
1.5%
Liverpool 57
 
1.1%
Glasgow 55
 
1.1%
Other values (38) 665
 
13.3%

Length

2024-02-04T09:22:00.413728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
london 3255
64.2%
paris 276
 
5.4%
manchester 180
 
3.6%
birmingham 144
 
2.8%
newcastle 110
 
2.2%
edinburgh 102
 
2.0%
cardiff 81
 
1.6%
bristol 75
 
1.5%
liverpool 57
 
1.1%
glasgow 55
 
1.1%
Other values (41) 732
 
14.4%

Most occurring characters

ValueCountFrequency (%)
n 7200
21.5%
o 7168
21.4%
d 3712
11.1%
L 3403
10.1%
e 1263
 
3.8%
r 1263
 
3.8%
i 1172
 
3.5%
a 1097
 
3.3%
s 889
 
2.7%
t 806
 
2.4%
Other values (33) 5557
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28339
84.5%
Uppercase Letter 5067
 
15.1%
Space Separator 98
 
0.3%
Other Punctuation 26
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 7200
25.4%
o 7168
25.3%
d 3712
13.1%
e 1263
 
4.5%
r 1263
 
4.5%
i 1172
 
4.1%
a 1097
 
3.9%
s 889
 
3.1%
t 806
 
2.8%
h 789
 
2.8%
Other values (13) 2980
10.5%
Uppercase Letter
ValueCountFrequency (%)
L 3403
67.2%
B 325
 
6.4%
P 294
 
5.8%
M 195
 
3.8%
N 192
 
3.8%
C 174
 
3.4%
E 128
 
2.5%
S 101
 
2.0%
G 64
 
1.3%
A 42
 
0.8%
Other values (8) 149
 
2.9%
Space Separator
ValueCountFrequency (%)
98
100.0%
Other Punctuation
ValueCountFrequency (%)
' 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33406
99.6%
Common 124
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 7200
21.6%
o 7168
21.5%
d 3712
11.1%
L 3403
10.2%
e 1263
 
3.8%
r 1263
 
3.8%
i 1172
 
3.5%
a 1097
 
3.3%
s 889
 
2.7%
t 806
 
2.4%
Other values (31) 5433
16.3%
Common
ValueCountFrequency (%)
98
79.0%
' 26
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 7200
21.5%
o 7168
21.4%
d 3712
11.1%
L 3403
10.1%
e 1263
 
3.8%
r 1263
 
3.8%
i 1172
 
3.5%
a 1097
 
3.3%
s 889
 
2.7%
t 806
 
2.4%
Other values (33) 5557
16.6%

creationdate
Date

MISSING 

Distinct4868
Distinct (%)> 99.9%
Missing130
Missing (%)2.6%
Memory size39.2 KiB
Minimum2024-01-20 10:45:09.267000
Maximum2024-01-25 00:44:35.807000
2024-02-04T09:22:00.533338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:00.677506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4999 
True
 
1
ValueCountFrequency (%)
False 4999
> 99.9%
True 1
 
< 0.1%
2024-02-04T09:22:00.787195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8828
Minimum3
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:22:00.874638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q17
median7
Q312
95-th percentile18
Maximum25
Range22
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.3493962
Coefficient of variation (CV)0.48964248
Kurtosis0.66252205
Mean8.8828
Median Absolute Deviation (MAD)3
Skewness0.9474632
Sum44414
Variance18.917248
MonotonicityNot monotonic
2024-02-04T09:22:00.979812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
7 1574
31.5%
10 641
12.8%
14 608
 
12.2%
3 514
 
10.3%
5 334
 
6.7%
12 224
 
4.5%
21 201
 
4.0%
4 195
 
3.9%
8 170
 
3.4%
9 162
 
3.2%
Other values (10) 377
 
7.5%
ValueCountFrequency (%)
3 514
 
10.3%
4 195
 
3.9%
5 334
 
6.7%
6 92
 
1.8%
7 1574
31.5%
8 170
 
3.4%
9 162
 
3.2%
10 641
12.8%
11 64
 
1.3%
12 224
 
4.5%
ValueCountFrequency (%)
25 1
 
< 0.1%
21 201
 
4.0%
20 25
 
0.5%
19 9
 
0.2%
18 23
 
0.5%
17 15
 
0.3%
16 30
 
0.6%
15 60
 
1.2%
14 608
12.2%
13 58
 
1.2%
Distinct698
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size452.2 KiB
2024-02-04T09:22:01.111648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length91
Median length60
Mean length35.5758
Min length3

Characters and Unicode

Total characters177879
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)3.5%

Sample

1st rowArts and Culture,History,Adventure
2nd rowArts and Culture,History,Adventure
3rd rowRelaxation,History
4th rowRelaxation,History
5th rowHistory,Nature,Food and Drink
ValueCountFrequency (%)
and 4909
29.8%
drink 845
 
5.1%
for 816
 
5.0%
arts 655
 
4.0%
food 580
 
3.5%
drink,nature 485
 
2.9%
relaxation,food 483
 
2.9%
good 419
 
2.5%
culture,history,food 401
 
2.4%
relaxation,arts 395
 
2.4%
Other values (370) 6462
39.3%
2024-02-04T09:22:01.386815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 14766
 
8.3%
r 14738
 
8.3%
t 14171
 
8.0%
a 13670
 
7.7%
n 12204
 
6.9%
, 11462
 
6.4%
11450
 
6.4%
i 11073
 
6.2%
d 10761
 
6.0%
e 10281
 
5.8%
Other values (18) 53303
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 133710
75.2%
Uppercase Letter 21257
 
12.0%
Other Punctuation 11462
 
6.4%
Space Separator 11450
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 14766
11.0%
r 14738
11.0%
t 14171
10.6%
a 13670
10.2%
n 12204
9.1%
i 11073
8.3%
d 10761
8.0%
e 10281
7.7%
u 7586
5.7%
s 5791
 
4.3%
Other values (8) 18669
14.0%
Uppercase Letter
ValueCountFrequency (%)
N 3490
16.4%
H 3201
15.1%
D 3135
14.7%
F 3135
14.7%
A 2859
13.4%
R 2847
13.4%
C 1774
8.3%
G 816
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 11462
100.0%
Space Separator
ValueCountFrequency (%)
11450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 154967
87.1%
Common 22912
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 14766
 
9.5%
r 14738
 
9.5%
t 14171
 
9.1%
a 13670
 
8.8%
n 12204
 
7.9%
i 11073
 
7.1%
d 10761
 
6.9%
e 10281
 
6.6%
u 7586
 
4.9%
s 5791
 
3.7%
Other values (16) 39926
25.8%
Common
ValueCountFrequency (%)
, 11462
50.0%
11450
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 14766
 
8.3%
r 14738
 
8.3%
t 14171
 
8.0%
a 13670
 
7.7%
n 12204
 
6.9%
, 11462
 
6.4%
11450
 
6.4%
i 11073
 
6.2%
d 10761
 
6.0%
e 10281
 
5.8%
Other values (18) 53303
30.0%

createtripformsubmission_extrainfo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size288.3 KiB
{}
4997 
{Good for vegans"}"
 
3

Length

Max length19
Median length2
Mean length2.0102
Min length2

Characters and Unicode

Total characters10051
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row{}
2nd row{}
3rd row{}
4th row{}
5th row{}

Common Values

ValueCountFrequency (%)
{} 4997
99.9%
{Good for vegans"}" 3
 
0.1%

Length

2024-02-04T09:22:01.507526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:01.593381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
4997
99.8%
good 3
 
0.1%
for 3
 
0.1%
vegans 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
{ 5000
49.7%
} 5000
49.7%
o 9
 
0.1%
6
 
0.1%
" 6
 
0.1%
G 3
 
< 0.1%
d 3
 
< 0.1%
f 3
 
< 0.1%
r 3
 
< 0.1%
v 3
 
< 0.1%
Other values (5) 15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Open Punctuation 5000
49.7%
Close Punctuation 5000
49.7%
Lowercase Letter 36
 
0.4%
Space Separator 6
 
0.1%
Other Punctuation 6
 
0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 9
25.0%
d 3
 
8.3%
f 3
 
8.3%
r 3
 
8.3%
v 3
 
8.3%
e 3
 
8.3%
g 3
 
8.3%
a 3
 
8.3%
n 3
 
8.3%
s 3
 
8.3%
Open Punctuation
ValueCountFrequency (%)
{ 5000
100.0%
Close Punctuation
ValueCountFrequency (%)
} 5000
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
" 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
G 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10012
99.6%
Latin 39
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 9
23.1%
G 3
 
7.7%
d 3
 
7.7%
f 3
 
7.7%
r 3
 
7.7%
v 3
 
7.7%
e 3
 
7.7%
g 3
 
7.7%
a 3
 
7.7%
n 3
 
7.7%
Common
ValueCountFrequency (%)
{ 5000
49.9%
} 5000
49.9%
6
 
0.1%
" 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
{ 5000
49.7%
} 5000
49.7%
o 9
 
0.1%
6
 
0.1%
" 6
 
0.1%
G 3
 
< 0.1%
d 3
 
< 0.1%
f 3
 
< 0.1%
r 3
 
< 0.1%
v 3
 
< 0.1%
Other values (5) 15
 
0.1%

failtimeseconds
Real number (ℝ)

MISSING 

Distinct127
Distinct (%)97.7%
Missing4870
Missing (%)97.4%
Infinite0
Infinite (%)0.0%
Mean593.91354
Minimum2.65
Maximum28218.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:22:01.688679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.65
5-th percentile4.2635
Q17.4425
median27.725
Q361.03
95-th percentile824.216
Maximum28218.37
Range28215.72
Interquartile range (IQR)53.5875

Descriptive statistics

Standard deviation3195.0613
Coefficient of variation (CV)5.3796741
Kurtosis53.573633
Mean593.91354
Median Absolute Deviation (MAD)21.27
Skewness7.1194547
Sum77208.76
Variance10208416
MonotonicityNot monotonic
2024-02-04T09:22:01.818887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.2 2
 
< 0.1%
6.83 2
 
< 0.1%
30.78 2
 
< 0.1%
341.21 1
 
< 0.1%
5.32 1
 
< 0.1%
45.31 1
 
< 0.1%
21.65 1
 
< 0.1%
48.94 1
 
< 0.1%
2.87 1
 
< 0.1%
10.69 1
 
< 0.1%
Other values (117) 117
 
2.3%
(Missing) 4870
97.4%
ValueCountFrequency (%)
2.65 1
< 0.1%
2.87 1
< 0.1%
2.91 1
< 0.1%
3.26 1
< 0.1%
3.56 1
< 0.1%
3.93 1
< 0.1%
4.25 1
< 0.1%
4.28 1
< 0.1%
4.6 1
< 0.1%
4.64 1
< 0.1%
ValueCountFrequency (%)
28218.37 1
< 0.1%
19266.32 1
< 0.1%
12860.31 1
< 0.1%
4860.36 1
< 0.1%
1260.54 1
< 0.1%
1041.07 1
< 0.1%
932.72 1
< 0.1%
691.6 1
< 0.1%
549.48 1
< 0.1%
542.18 1
< 0.1%

failureindurationserrorcount
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.3 KiB
0
5000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5000
100.0%

Length

2024-02-04T09:22:01.931785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:02.009252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5000
100.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
5000 
ValueCountFrequency (%)
False 5000
100.0%
2024-02-04T09:22:02.074792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

firstchoiceaccommodationunavailablecount
Real number (ℝ)

SKEWED  ZEROS 

Distinct36
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.404
Minimum0
Maximum396
Zeros1760
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:22:02.169271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum396
Range396
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.920517
Coefficient of variation (CV)9.2026478
Kurtosis555.84932
Mean1.404
Median Absolute Deviation (MAD)0
Skewness22.721643
Sum7020
Variance166.93977
MonotonicityNot monotonic
2024-02-04T09:22:02.296932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 3042
60.8%
0 1760
35.2%
2 106
 
2.1%
3 24
 
0.5%
5 12
 
0.2%
4 10
 
0.2%
6 6
 
0.1%
18 4
 
0.1%
25 3
 
0.1%
9 3
 
0.1%
Other values (26) 30
 
0.6%
ValueCountFrequency (%)
0 1760
35.2%
1 3042
60.8%
2 106
 
2.1%
3 24
 
0.5%
4 10
 
0.2%
5 12
 
0.2%
6 6
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 3
 
0.1%
ValueCountFrequency (%)
396 1
< 0.1%
338 2
< 0.1%
314 1
< 0.1%
294 1
< 0.1%
258 1
< 0.1%
226 1
< 0.1%
208 1
< 0.1%
182 1
< 0.1%
170 1
< 0.1%
122 1
< 0.1%

noavailabledotwaccommodationerrorcount
Real number (ℝ)

SKEWED  ZEROS 

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9886
Minimum0
Maximum1118
Zeros4861
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:22:02.418279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1118
Range1118
Interquartile range (IQR)0

Descriptive statistics

Standard deviation37.3858
Coefficient of variation (CV)18.80006
Kurtosis514.53629
Mean1.9886
Median Absolute Deviation (MAD)0
Skewness22.066475
Sum9943
Variance1397.698
MonotonicityNot monotonic
2024-02-04T09:22:02.531811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 4861
97.2%
1 65
 
1.3%
2 24
 
0.5%
4 8
 
0.2%
5 7
 
0.1%
6 5
 
0.1%
3 5
 
0.1%
55 2
 
< 0.1%
48 2
 
< 0.1%
348 1
 
< 0.1%
Other values (20) 20
 
0.4%
ValueCountFrequency (%)
0 4861
97.2%
1 65
 
1.3%
2 24
 
0.5%
3 5
 
0.1%
4 8
 
0.2%
5 7
 
0.1%
6 5
 
0.1%
9 1
 
< 0.1%
12 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
1118 1
< 0.1%
920 1
< 0.1%
919 1
< 0.1%
846 1
< 0.1%
811 1
< 0.1%
792 1
< 0.1%
706 1
< 0.1%
681 1
< 0.1%
622 1
< 0.1%
581 1
< 0.1%

nofamilymanualfallbackserrorcount
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0392
Minimum0
Maximum62
Zeros4995
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:22:02.626008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3477487
Coefficient of variation (CV)34.381345
Kurtosis1477.9198
Mean0.0392
Median Absolute Deviation (MAD)0
Skewness37.45283
Sum196
Variance1.8164266
MonotonicityNot monotonic
2024-02-04T09:22:02.718299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 4995
99.9%
62 1
 
< 0.1%
38 1
 
< 0.1%
50 1
 
< 0.1%
12 1
 
< 0.1%
34 1
 
< 0.1%
ValueCountFrequency (%)
0 4995
99.9%
12 1
 
< 0.1%
34 1
 
< 0.1%
38 1
 
< 0.1%
50 1
 
< 0.1%
62 1
 
< 0.1%
ValueCountFrequency (%)
62 1
 
< 0.1%
50 1
 
< 0.1%
38 1
 
< 0.1%
34 1
 
< 0.1%
12 1
 
< 0.1%
0 4995
99.9%

nogoodscoreerrorcount
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size283.3 KiB
0
4943 
1
 
38
2
 
18
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4943
98.9%
1 38
 
0.8%
2 18
 
0.4%
4 1
 
< 0.1%

Length

2024-02-04T09:22:02.825373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:03.090042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4943
98.9%
1 38
 
0.8%
2 18
 
0.4%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 4943
98.9%
1 38
 
0.8%
2 18
 
0.4%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4943
98.9%
1 38
 
0.8%
2 18
 
0.4%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4943
98.9%
1 38
 
0.8%
2 18
 
0.4%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4943
98.9%
1 38
 
0.8%
2 18
 
0.4%
4 1
 
< 0.1%

norouteserrorcount
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1204
Minimum0
Maximum134
Zeros3135
Zeros (%)62.7%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:22:03.194888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum134
Range134
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.2943209
Coefficient of variation (CV)3.8328462
Kurtosis428.16635
Mean1.1204
Median Absolute Deviation (MAD)0
Skewness17.574793
Sum5602
Variance18.441192
MonotonicityNot monotonic
2024-02-04T09:22:03.332951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 3135
62.7%
1 899
 
18.0%
2 428
 
8.6%
3 200
 
4.0%
4 90
 
1.8%
5 51
 
1.0%
9 44
 
0.9%
6 34
 
0.7%
10 23
 
0.5%
7 21
 
0.4%
Other values (26) 75
 
1.5%
ValueCountFrequency (%)
0 3135
62.7%
1 899
 
18.0%
2 428
 
8.6%
3 200
 
4.0%
4 90
 
1.8%
5 51
 
1.0%
6 34
 
0.7%
7 21
 
0.4%
8 21
 
0.4%
9 44
 
0.9%
ValueCountFrequency (%)
134 1
< 0.1%
120 1
< 0.1%
100 1
< 0.1%
93 1
< 0.1%
65 1
< 0.1%
60 1
< 0.1%
50 1
< 0.1%
49 1
< 0.1%
42 1
< 0.1%
40 1
< 0.1%

jotform_onewaytrip
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5000
Missing (%)100.0%
Memory size39.2 KiB

overnightreductionerrorcount
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.3 KiB
0
5000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5000
100.0%

Length

2024-02-04T09:22:03.453174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:03.532276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5000
100.0%
Distinct403
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Minimum2024-01-30 00:00:00
Maximum2032-03-20 00:00:00
2024-02-04T09:22:03.631986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:03.770197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.174
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:22:03.874290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45493449
Coefficient of variation (CV)0.38750809
Kurtosis16.492839
Mean1.174
Median Absolute Deviation (MAD)0
Skewness3.3105235
Sum5870
Variance0.20696539
MonotonicityNot monotonic
2024-02-04T09:22:03.967346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4255
85.1%
2 639
 
12.8%
3 97
 
1.9%
6 4
 
0.1%
4 3
 
0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
1 4255
85.1%
2 639
 
12.8%
3 97
 
1.9%
4 3
 
0.1%
5 2
 
< 0.1%
6 4
 
0.1%
ValueCountFrequency (%)
6 4
 
0.1%
5 2
 
< 0.1%
4 3
 
0.1%
3 97
 
1.9%
2 639
 
12.8%
1 4255
85.1%

substituteaccommodationunavailablecount
Real number (ℝ)

SKEWED  ZEROS 

Distinct34
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7678
Minimum0
Maximum396
Zeros4773
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:22:04.073072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum396
Range396
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.93792
Coefficient of variation (CV)16.850638
Kurtosis557.31234
Mean0.7678
Median Absolute Deviation (MAD)0
Skewness22.774483
Sum3839
Variance167.38976
MonotonicityNot monotonic
2024-02-04T09:22:04.197579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 4773
95.5%
1 126
 
2.5%
2 32
 
0.6%
4 11
 
0.2%
5 8
 
0.2%
6 7
 
0.1%
3 6
 
0.1%
18 4
 
0.1%
25 3
 
0.1%
338 2
 
< 0.1%
Other values (24) 28
 
0.6%
ValueCountFrequency (%)
0 4773
95.5%
1 126
 
2.5%
2 32
 
0.6%
3 6
 
0.1%
4 11
 
0.2%
5 8
 
0.2%
6 7
 
0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
396 1
< 0.1%
338 2
< 0.1%
314 1
< 0.1%
294 1
< 0.1%
258 1
< 0.1%
226 1
< 0.1%
208 1
< 0.1%
182 1
< 0.1%
170 1
< 0.1%
122 1
< 0.1%

success
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
4870 
False
 
130
ValueCountFrequency (%)
True 4870
97.4%
False 130
 
2.6%
2024-02-04T09:22:04.300782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

timeouterrorcount
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size283.3 KiB
0
4576 
1
 
374
2
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4576
91.5%
1 374
 
7.5%
2 50
 
1.0%

Length

2024-02-04T09:22:04.387750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:04.470965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4576
91.5%
1 374
 
7.5%
2 50
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 4576
91.5%
1 374
 
7.5%
2 50
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4576
91.5%
1 374
 
7.5%
2 50
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4576
91.5%
1 374
 
7.5%
2 50
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4576
91.5%
1 374
 
7.5%
2 50
 
1.0%

transportunavailableerrorcount
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size283.3 KiB
0
4998 
2
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4998
> 99.9%
2 1
 
< 0.1%
1 1
 
< 0.1%

Length

2024-02-04T09:22:04.563637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:04.648230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4998
> 99.9%
2 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 4998
> 99.9%
2 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4998
> 99.9%
2 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4998
> 99.9%
2 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4998
> 99.9%
2 1
 
< 0.1%
1 1
 
< 0.1%

tripbuildtimeseconds
Real number (ℝ)

MISSING  SKEWED 

Distinct2290
Distinct (%)47.0%
Missing130
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean18.116002
Minimum0.47
Maximum2052.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-02-04T09:22:04.755324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.47
5-th percentile3.45
Q17.8525
median12.205
Q317.8575
95-th percentile33.5685
Maximum2052.56
Range2052.09
Interquartile range (IQR)10.005

Descriptive statistics

Standard deviation51.406612
Coefficient of variation (CV)2.8376356
Kurtosis733.79269
Mean18.116002
Median Absolute Deviation (MAD)4.845
Skewness23.380406
Sum88224.93
Variance2642.6398
MonotonicityNot monotonic
2024-02-04T09:22:04.883588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.02 35
 
0.7%
30.01 22
 
0.4%
6.92 9
 
0.2%
7.36 9
 
0.2%
16.58 8
 
0.2%
9 8
 
0.2%
11.32 8
 
0.2%
13.08 8
 
0.2%
11.06 8
 
0.2%
30 8
 
0.2%
Other values (2280) 4747
94.9%
(Missing) 130
 
2.6%
ValueCountFrequency (%)
0.47 1
< 0.1%
0.87 1
< 0.1%
0.89 1
< 0.1%
0.99 1
< 0.1%
1.01 1
< 0.1%
1.02 1
< 0.1%
1.04 1
< 0.1%
1.09 1
< 0.1%
1.1 1
< 0.1%
1.12 1
< 0.1%
ValueCountFrequency (%)
2052.56 1
< 0.1%
1443.41 1
< 0.1%
1178.65 1
< 0.1%
1058.15 1
< 0.1%
824.52 1
< 0.1%
632.14 1
< 0.1%
519.46 1
< 0.1%
475.17 1
< 0.1%
338.64 1
< 0.1%
310.94 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4377 
True
623 
ValueCountFrequency (%)
False 4377
87.5%
True 623
 
12.5%
2024-02-04T09:22:04.979609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Interactions

2024-02-04T09:21:55.473089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:41.012077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.297114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:43.427691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.671106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.763661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.929093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.088395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.338755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.517906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:51.703495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.056296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.241045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:55.559829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:41.155635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.387708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:43.515746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.758089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.850127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.010701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.182223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.433010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.614538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:51.964135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.147248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.338900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:55.644008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:41.248550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.474373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:43.604756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.845825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.943048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.087980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.278058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.522790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.704878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.051076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.234220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.434096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:55.730088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:41.330456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.556585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:43.682959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.925275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.029657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.166263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.371429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.609564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.792170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.137577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.317327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.523129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:55.813962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:41.415583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.642008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:43.898703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.003597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.154106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.238272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.471360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.696867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.879855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.225237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.399936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.616096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:56.066263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:41.505020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.723833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:43.980747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.084139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.230796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.311328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.571715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.782929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.968224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.314659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.484806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.708100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:56.133469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:41.592734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.797636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.052967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.155263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.304625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.385455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.654676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.863737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:51.045578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.396192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.558517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.791695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:56.220748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:41.743010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.895273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.146508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.249199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.396876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.472573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.758555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.970000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:51.145701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.497202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.657773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.897767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:56.304816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:41.830880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.982097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.233278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.331530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.480705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.546350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.852771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.059018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:51.237288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.589109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.744936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.993712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:56.401081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:41.922508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:43.070212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.322586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.418487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.567562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.626712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.952263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.150516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:51.328065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.684857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.837049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:55.097672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:56.487762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.014859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:43.160457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.410648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.506284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.660862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.708723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.052745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.249047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:51.423107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.782795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:53.939227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:55.195309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:56.574776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.103248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:43.247668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.495752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.590049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.745245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:47.785513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.148266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.337295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:51.512358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.873050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.046962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:55.287769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:56.669506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:42.207596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:43.347595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:44.588278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:45.683534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:46.843594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:48.022036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:49.254300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:50.437701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:51.612839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:52.975736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:54.156065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:21:55.386437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-02-04T09:21:56.837487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-04T09:21:57.278129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

createtripidregioncreatetripid_regionrequestdateaborteddataerrorcountaccommodationunavailableerrorcountcreatetripformsubmission_adultcountbadproportionerrorcountcreatetripformsubmission_childagescreatetripformsubmission_childcountcreatetripformsubmission_closestcity_idcreatetripformsubmission_closestcitycreationdatecreatetripformsubmission_directjourneycreatetripformsubmission_durationdaysarray_to_stringcreatetripformsubmission_extrainfofailtimesecondsfailureindurationserrorcountcreatetripformsubmission_fastjourneyfirstchoiceaccommodationunavailablecountnoavailabledotwaccommodationerrorcountnofamilymanualfallbackserrorcountnogoodscoreerrorcountnorouteserrorcountjotform_onewaytripovernightreductionerrorcountcreatetripformsubmission_preferreddatecreatetripformsubmission_roomcountsubstituteaccommodationunavailablecountsuccesstimeouterrorcounttransportunavailableerrorcounttripbuildtimesecondscreatetripformsubmission_twinroompreferred
0891585467457763Southern Italy891585467457763_Southern Italy2024-01-20 10:44:58.3680020{}026London2024-01-20 10:45:09.267False12Arts and Culture,History,Adventure{}NaN0False00001NaN02024-05-1610True009.51False
1891585467457763Italian Riviera891585467457763_Italian Riviera2024-01-20 10:44:58.3680020{}026London2024-01-20 10:45:11.821False12Arts and Culture,History,Adventure{}NaN0False10000NaN02024-05-1610True0012.06False
2856446659120383Highlands & Islands856446659120383_Highlands & Islands2024-01-20 10:45:24.46700214{}0833158856CoventryNaTFalse3Relaxation,History{}11.650False11019NaN02024-05-1311False00NaNFalse
3856446659120383East Highlands856446659120383_East Highlands2024-01-20 10:45:24.4670024{}0833158856Coventry2024-01-20 10:45:29.964False3Relaxation,History{}NaN0False00000NaN02024-05-1310True004.79False
4643110738642468Yorkshire643110738642468_Yorkshire2024-01-20 10:46:38.6690020{}028Birmingham2024-01-20 10:46:45.532False4History,Nature,Food and Drink{}NaN0False10000NaN02024-08-1510True005.79True
5882120083576575Alsace882120083576575_Alsace2024-01-20 10:48:42.1990020{}026London2024-01-20 10:48:46.012False5Relaxation,History,Food and Drink,Nature,Arts and Culture{}NaN0False00002NaN02024-05-3010True002.93True
6749696652786878Belgium749696652786878_Belgium2024-01-20 10:50:58.7820020{}042771065Liverpool2024-01-20 10:51:10.961False7Food and Drink,History,Nature,Arts and Culture,Nightlife{}NaN0False10000NaN02024-03-1610True0011.48False
7639440762388119Corsica639440762388119_Corsica2024-01-20 10:53:39.1280020{13,11}226London2024-01-20 10:53:55.616False14Good for kids{}NaN0False10000NaN02024-08-1110True0015.66False
8639440762388119Hauts-de-France639440762388119_Hauts-de-France2024-01-20 10:53:39.1280020{13,11}226London2024-01-20 10:53:55.467False14Good for kids{}NaN0False10000NaN02024-08-1110True0015.50False
9747514586085701Southern Italy747514586085701_Southern Italy2024-01-20 10:58:03.4170020{}022370660Newcastle2024-01-20 10:58:09.650False21Relaxation,History,Food and Drink{}NaN0False000010NaN02025-07-0110True005.22False
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